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Title: Heat transport in liquid water from first-principles and deep neural network simulations

Abstract

In this work, we compute the thermal conductivity of water within linear response theory from equilibrium molecular dynamics simulations, by adopting two different approaches. In one, the potential energy surface (PES) is derived on the fly from the electronic ground state of density functional theory (DFT) and the corresponding analytical expression is used for the energy flux. In the other, the PES is represented by a deep neural network (DNN) trained on DFT data, whereby the PES has an explicit local decomposition and the energy flux takes a particularly simple expression. By virtue of a gauge invariance principle, established by Marcolongo, Umari, and Baroni, the two approaches should be equivalent if the PES were reproduced accurately by the DNN model. We test this hypothesis by calculating the thermal conductivity, at the GGA (PBE) level of theory, using the direct formulation and its DNN proxy, finding that both approaches yield the same conductivity, in excess of the experimental value by approximately 60%. Besides being numerically much more efficient than its direct DFT counterpart, the DNN scheme has the advantage of being easily applicable to more sophisticated DFT approximations, such as meta-GGA and hybrid functionals, for which it would be hard tomore » derive analytically the expression of the energy flux. We find in this way that a DNN model, trained on meta-GGA (SCAN) data, reduces the deviation from experiment of the predicted thermal conductivity by about 50%, leaving the question open as to whether the residual error is due to deficiencies of the functional, to a neglect of nuclear quantum effects in the atomic dynamics, or, likely, to a combination of the two.« less

Authors:
ORCiD logo [1];  [2];  [1];  [3];  [2]; ORCiD logo [4]
+ Show Author Affiliations
  1. International School for Advanced Studies (SISSA), Trieste (Italy)
  2. Princeton Univ., NJ (United States)
  3. Inst. of Applied Physics and Computational Mathematics (IAPCM), Beijing (China)
  4. International School for Advanced Studies (SISSA), Trieste (Italy); Istituto Officina dei Materiali, Trieste (Italy)
Publication Date:
Research Org.:
Princeton Univ., NJ (United States)
Sponsoring Org.:
USDOE Office of Science (SC); National Science Foundation of China (NSFC)
OSTI Identifier:
1994993
Alternate Identifier(s):
OSTI ID: 1979686
Grant/Contract Number:  
SC0019394; 11871110
Resource Type:
Accepted Manuscript
Journal Name:
Physical Review. B
Additional Journal Information:
Journal Volume: 104; Journal Issue: 22; Journal ID: ISSN 2469-9950
Publisher:
American Physical Society (APS)
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; thermal conductivity; thermal properties; water; density functional theory; linear response theory; machine learning; molecular dynamics; Materials Science; Physics

Citation Formats

Tisi, Davide, Zhang, Linfeng, Bertossa, Riccardo, Wang, Han, Car, Roberto, and Baroni, Stefano. Heat transport in liquid water from first-principles and deep neural network simulations. United States: N. p., 2021. Web. doi:10.1103/physrevb.104.224202.
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Tisi, Davide, Zhang, Linfeng, Bertossa, Riccardo, Wang, Han, Car, Roberto, & Baroni, Stefano. Heat transport in liquid water from first-principles and deep neural network simulations. United States. https://doi.org/10.1103/physrevb.104.224202
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Tisi, Davide, Zhang, Linfeng, Bertossa, Riccardo, Wang, Han, Car, Roberto, and Baroni, Stefano. Mon . "Heat transport in liquid water from first-principles and deep neural network simulations". United States. https://doi.org/10.1103/physrevb.104.224202. https://www.osti.gov/servlets/purl/1994993.
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@article{osti_1994993,
title = {Heat transport in liquid water from first-principles and deep neural network simulations},
author = {Tisi, Davide and Zhang, Linfeng and Bertossa, Riccardo and Wang, Han and Car, Roberto and Baroni, Stefano},
abstractNote = {In this work, we compute the thermal conductivity of water within linear response theory from equilibrium molecular dynamics simulations, by adopting two different approaches. In one, the potential energy surface (PES) is derived on the fly from the electronic ground state of density functional theory (DFT) and the corresponding analytical expression is used for the energy flux. In the other, the PES is represented by a deep neural network (DNN) trained on DFT data, whereby the PES has an explicit local decomposition and the energy flux takes a particularly simple expression. By virtue of a gauge invariance principle, established by Marcolongo, Umari, and Baroni, the two approaches should be equivalent if the PES were reproduced accurately by the DNN model. We test this hypothesis by calculating the thermal conductivity, at the GGA (PBE) level of theory, using the direct formulation and its DNN proxy, finding that both approaches yield the same conductivity, in excess of the experimental value by approximately 60%. Besides being numerically much more efficient than its direct DFT counterpart, the DNN scheme has the advantage of being easily applicable to more sophisticated DFT approximations, such as meta-GGA and hybrid functionals, for which it would be hard to derive analytically the expression of the energy flux. We find in this way that a DNN model, trained on meta-GGA (SCAN) data, reduces the deviation from experiment of the predicted thermal conductivity by about 50%, leaving the question open as to whether the residual error is due to deficiencies of the functional, to a neglect of nuclear quantum effects in the atomic dynamics, or, likely, to a combination of the two.},
doi = {10.1103/physrevb.104.224202},
journal = {Physical Review. B},
number = 22,
volume = 104,
place = {United States},
year = {Mon Dec 13 00:00:00 EST 2021},
month = {Mon Dec 13 00:00:00 EST 2021}
}
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Works referenced in this record:

Generalized Gradient Approximation Made Simple
journal, October 1996

  • Perdew, John P.; Burke, Kieron; Ernzerhof, Matthias
  • Physical Review Letters, Vol. 77, Issue 18, p. 3865-3868
  • DOI: 10.1103/PhysRevLett.77.3865

Intrinsic lattice thermal conductivity of semiconductors from first principles
journal, December 2007

  • Broido, D. A.; Malorny, M.; Birner, G.
  • Applied Physics Letters, Vol. 91, Issue 23
  • DOI: 10.1063/1.2822891

Heat and charge transport in H2O at ice-giant conditions from ab initio molecular dynamics simulations
journal, July 2020

Statistical Mechanics of Nonequilibrium Liquids
book, January 2007

Theory and Numerical Simulation of Heat Transport in Multicomponent Systems
journal, June 2019

Deep neural network for the dielectric response of insulators
journal, July 2020

Microscopic theory and quantum simulation of atomic heat transport
journal, October 2015

  • Marcolongo, Aris; Umari, Paolo; Baroni, Stefano
  • Nature Physics, Vol. 12, Issue 1
  • DOI: 10.1038/nphys3509

QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials
journal, September 2009

  • Giannozzi, Paolo; Baroni, Stefano; Bonini, Nicola
  • Journal of Physics: Condensed Matter, Vol. 21, Issue 39, Article No. 395502
  • DOI: 10.1088/0953-8984/21/39/395502

On the phase diagram of water with density functional theory potentials: The melting temperature of ice Ih with the Perdew–Burke–Ernzerhof and Becke–Lee–Yang–Parr functionals
journal, June 2009

  • Yoo, Soohaeng; Zeng, Xiao Cheng; Xantheas, Sotiris S.
  • The Journal of Chemical Physics, Vol. 130, Issue 22
  • DOI: 10.1063/1.3153871

Fast Parallel Algorithms for Short-Range Molecular Dynamics
journal, March 1995

Computer Simulation of Liquids
book, June 2017

First-principles Green-Kubo method for thermal conductivity calculations
journal, July 2017

Optimized norm-conserving Vanderbilt pseudopotentials
journal, August 2013

Towards an assessment of the accuracy of density functional theory for first principles simulations of water. II
journal, September 2004

  • Schwegler, Eric; Grossman, Jeffrey C.; Gygi, François
  • The Journal of Chemical Physics, Vol. 121, Issue 11
  • DOI: 10.1063/1.1782074

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
journal, July 2018

Static and dynamical properties of heavy water at ambient conditions from first-principles molecular dynamics
journal, May 2005

  • Sit, P. H. -L.; Marzari, Nicola
  • The Journal of Chemical Physics, Vol. 122, Issue 20
  • DOI: 10.1063/1.1908913

Thermal and Tidal Evolution of Uranus with a Growing Frozen Core
journal, November 2021

  • Stixrude, Lars; Baroni, Stefano; Grasselli, Federico
  • The Planetary Science Journal, Vol. 2, Issue 6
  • DOI: 10.3847/psj/ac2a47

Thermal Conductivity of Periclase (MgO) from First Principles
journal, May 2010

Unified Approach for Molecular Dynamics and Density-Functional Theory
journal, November 1985

Standard Reference Data for the Thermal Conductivity of Water
journal, May 1995

  • Ramires, Maria L. V.; Nieto de Castro, Carlos A.; Nagasaka, Yuchi
  • Journal of Physical and Chemical Reference Data, Vol. 24, Issue 3
  • DOI: 10.1063/1.555963

Lattice Anharmonicity and Thermal Conductivity from Compressive Sensing of First-Principles Calculations
journal, October 2014

Topological quantization and gauge invariance of charge transport in liquid insulators
journal, July 2019

Deep learning of accurate force field of ferroelectric HfO 2
journal, January 2021

Quantization of particle transport
journal, May 1983

Q uantum ESPRESSO toward the exascale
journal, April 2020

  • Giannozzi, Paolo; Baseggio, Oscar; Bonfà, Pietro
  • The Journal of Chemical Physics, Vol. 152, Issue 15
  • DOI: 10.1063/5.0005082

Self-Consistent Equations Including Exchange and Correlation Effects
journal, November 1965

Ab initio theory and modeling of water
journal, September 2017

  • Chen, Mohan; Ko, Hsin-Yu; Remsing, Richard C.
  • Proceedings of the National Academy of Sciences, Vol. 114, Issue 41
  • DOI: 10.1073/pnas.1712499114

Markoff Random Processes and the Statistical Mechanics of Time‐Dependent Phenomena. II. Irreversible Processes in Fluids
journal, March 1954

  • Green, Melville S.
  • The Journal of Chemical Physics, Vol. 22, Issue 3
  • DOI: 10.1063/1.1740082

Ab initio phase diagram and nucleation of gallium
journal, May 2020

Learning representations by back-propagating errors
journal, October 1986

  • Rumelhart, David E.; Hinton, Geoffrey E.; Williams, Ronald J.
  • Nature, Vol. 323, Issue 6088
  • DOI: 10.1038/323533a0

Ab Initio Green-Kubo Approach for the Thermal Conductivity of Solids
journal, April 2017

Accurate thermal conductivities from optimally short molecular dynamics simulations
journal, November 2017

Phase Diagram of a Deep Potential Water Model
journal, June 2021

Invariance principles in the theory and computation of transport coefficients
journal, August 2021

System-Size Dependence of Diffusion Coefficients and Viscosities from Molecular Dynamics Simulations with Periodic Boundary Conditions
journal, October 2004

  • Yeh, In-Chul; Hummer, Gerhard
  • The Journal of Physical Chemistry B, Vol. 108, Issue 40
  • DOI: 10.1021/jp0477147

Phase Equilibrium of Water with Hexagonal and Cubic Ice Using the SCAN Functional
journal, April 2021

  • Piaggi, Pablo M.; Panagiotopoulos, Athanassios Z.; Debenedetti, Pablo G.
  • Journal of Chemical Theory and Computation, Vol. 17, Issue 5
  • DOI: 10.1021/acs.jctc.1c00041

A simple nonequilibrium molecular dynamics method for calculating the thermal conductivity
journal, April 1997

  • Müller-Plathe, Florian
  • The Journal of Chemical Physics, Vol. 106, Issue 14
  • DOI: 10.1063/1.473271

Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
journal, April 2007

Towards an assessment of the accuracy of density functional theory for first principles simulations of water
journal, January 2004

  • Grossman, Jeffrey C.; Schwegler, Eric; Draeger, Erik W.
  • The Journal of Chemical Physics, Vol. 120, Issue 1
  • DOI: 10.1063/1.1630560

Accurate Deep Potential model for the Al–Cu–Mg alloy in the full concentration space*
journal, May 2021

QEHeat: An open-source energy flux calculator for the computation of heat-transport coefficients from first principles
journal, December 2021

  • Marcolongo, Aris; Bertossa, Riccardo; Tisi, Davide
  • Computer Physics Communications, Vol. 269
  • DOI: 10.1016/j.cpc.2021.108090

Gauge Fixing for Heat-Transport Simulations
journal, April 2020

  • Marcolongo, Aris; Ercole, Loris; Baroni, Stefano
  • Journal of Chemical Theory and Computation, Vol. 16, Issue 5
  • DOI: 10.1021/acs.jctc.9b01174

Gauge Invariance of Thermal Transport Coefficients
journal, April 2016

  • Ercole, Loris; Marcolongo, Aris; Umari, Paolo
  • Journal of Low Temperature Physics, Vol. 185, Issue 1-2
  • DOI: 10.1007/s10909-016-1617-6

Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
journal, April 2010

LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales
journal, February 2022

  • Thompson, Aidan P.; Aktulga, H. Metin; Berger, Richard
  • Computer Physics Communications, Vol. 271
  • DOI: 10.1016/j.cpc.2021.108171

Atomistic simulations of the thermal conductivity of liquids
journal, May 2020

Deep potential generation scheme and simulation protocol for the Li 10 GeP 2 S 12 -type superionic conductors
journal, March 2021

  • Huang, Jianxing; Zhang, Linfeng; Wang, Han
  • The Journal of Chemical Physics, Vol. 154, Issue 9
  • DOI: 10.1063/5.0041849

Signatures of a liquid–liquid transition in an ab initio deep neural network model for water
journal, October 2020

  • Gartner, Thomas E.; Zhang, Linfeng; Piaggi, Pablo M.
  • Proceedings of the National Academy of Sciences, Vol. 117, Issue 42
  • DOI: 10.1073/pnas.2015440117

Stationary nonequilibrium states by molecular dynamics. Fourier's law
journal, May 1982

  • Tenenbaum, Alexander; Ciccotti, Giovanni; Gallico, Renato
  • Physical Review A, Vol. 25, Issue 5
  • DOI: 10.1103/PhysRevA.25.2778

Zur kinetischen Theorie der Wärmeleitung in Kristallen
journal, January 1929

Advanced capabilities for materials modelling with Quantum ESPRESSO
journal, October 2017

  • Giannozzi, P.; Andreussi, O.; Brumme, T.
  • Journal of Physics: Condensed Matter, Vol. 29, Issue 46
  • DOI: 10.1088/1361-648X/aa8f79

Modeling Liquid Water by Climbing up Jacob’s Ladder in Density Functional Theory Facilitated by Using Deep Neural Network Potentials
journal, September 2021

  • Zhang, Chunyi; Tang, Fujie; Chen, Mohan
  • The Journal of Physical Chemistry B, Vol. 125, Issue 41
  • DOI: 10.1021/acs.jpcb.1c03884

Simulating Diffusion Properties of Solid‐State Electrolytes via a Neural Network Potential: Performance and Training Scheme
journal, December 2019

  • Marcolongo, Aris; Binninger, Tobias; Zipoli, Federico
  • ChemSystemsChem, Vol. 2, Issue 3
  • DOI: 10.1002/syst.201900031
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